Table REF  presents average Friedman test ranks [1]} for each classifier on each tested metric (the lower the rank the better). Setting a significance level \(\alpha =0.05\) , we can reject the null hypothesis of the Friedman test stating that the performances of the classifiers are equivalent only for the number of selected features, their standard deviation, and processing time. This means that the final predictive performance of classifiers was comparable when using any of the tested feature selectors, yet some them were significantly faster and chose less features than others. The critical distance plots for the Nemenyi post-hoc test [1]} (Figures REF  and REF ) indicate that FRFE is significantly faster than all the other selectors except RFE\(_F\)  (which was forced to use the same number of evaluations). Moreover, \(k\) -SRFE selectors tend to choose smaller feature subsets than their RFE counterparts.
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